MAIF/shapash

A game theoretic approach to explain the output of any machine learning model.

Interpretability and explainability of data and machine learning models

Fit interpretable models. Explain blackbox machine learning.

Lime: Explaining the predictions of any machine learning classifier

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.

Model interpretability and understanding for PyTorch

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

A collection of research materials on explainable AI/ML